1. Field of the Invention
The present invention generally relates to multiple-input-multiple-output (MIMO) communication systems, and particularly relates to determining combining weights for MIMO receivers.
2. Background
MIMO systems generally use one or more channelization techniques to distinguish the different transmitted information streams. For example, MIMO systems based on Code Division Multiple Access (CDMA) signal types use orthogonal code sets to define different information channels, where each such channel carries individually resolvable data. MIMO systems based on Orthogonal Frequency Division Multiplexing (OFDM) signal types use different sub-carriers in a set of OFDM sub-carriers to define different channels. However, these and other types of MIMO systems commonly reuse channelization codes and or channelization frequencies across transmitted data streams, possibly on distinct antennas, leading to so-called “reuse” interference between streams at the targeted receivers-broadly denoted as “channel reuse cross-stream interference.”
Robust interference suppression, such as that generally afforded by Generalized Rake (G-Rake) receivers, equalization receivers, and other types of linear interference canceling receivers, must include suppression of channel reuse cross-stream interference in MIMO and other contexts where such interference arises. However, the consideration of channel reuse cross-stream interference by a receiver in interference suppression processing introduces potentially significant computational complexity. Further, existing signal processing algorithms for determining interference-suppression combining weights may not converge in a practical number of iterations (or at all) when channel reuse cross-stream interference is considered.
For example, it is known in non-MIMO G-Rake processing contexts how to determine combining weights from signal impairment correlation estimates, where the various components of impairment include same-cell interference (i.e., Inter-Symbol Interference or ISI), other-cell interference, and thermal noise. The overall impairment correlation estimate, which represents the correlation of received signal impairment across G-Rake despreading fingers, thus includes a same-cell impairment correlation term, an other-cell impairment correlation term, and a thermal noise impairment term. (Some approaches to impairment correlation modeling collapse the other-cell and thermal noise terms into a combined term.)
Extending the above context to MIMO systems with channel reuse, received signal impairment includes channel reuse cross-stream interference. Therefore, the expression of received signal impairment correlations used for combining weight generation must include a corresponding impairment correlation term. More particularly, considering channel reuse cross-stream interference in the impairment correlation model introduces, in at least some approaches to modeling impairment correlations, a rank-one component making the combining weight solution more difficult (or practically impossible) to determine. Further, the need for calculating mode-dependent combining weights for MIMO operation—i.e., the computation of combining weights for each of two or more possible MIMO transmission modes—exacerbates the problems raised by the increased complexity of the underlying combining weight calculations.